Multi-spectral image compression with bounded loss

ABSTRACT

A system and method for generating bounded-loss color transformations employed in the compression and decompression of multi-spectral images is provided. The bounded-loss color space transformations provide transformations from a first color space to a second color space and back to the first color space with bounded loss or error. The bounded loss or error allows for the near lossless compression and reconstruction of multi-spectral images transformed from a first color space to a second color space and ultimately back to the first color space.

FIELD OF THE INVENTION

The invention relates generally to the compression of digital images,more particularly, to methods for compressing multi-spectral images indifferent spectral spaces and transforming images from one spectralspace to another with bounded loss.

BACKGROUND OF THE INVENTION

A multi-spectral image is a collection of two or more monochrome imagesof the same scene. Multi-spectral images can be described in any one ofa plurality of known spectral or color spaces. For example, onewell-known multi-spectral image is an RGB color image. An RGB colorimage consists of a red, a green, and a blue component and, thus, theimage is said to be described in RGB spectral space. Other spectralspaces (sometimes hereinafter referred as color space(s)) include theCIE (Commission International de L'Eclairage) L* a* b*, CIE XYZ, CIE L*u* v*, CIE YUV, CMY (Cyan, Magenta, Yellow), CMYK (Cyan, Magenta,Yellow, black), CCIR (Comite Consultatif International desRadiocommunications) 601 YCbCr, YIQ, HIS, and HSV spectral spaces. SeeTelevision Engineering Handbook, Featuring HDTV Systems, Revised Editionby K. Blair Benson, revised by Jerry C. Whitaker (McGraw-Hill, 1992) formore information on color spaces.

Digital multi-spectral images, as well as all digital images, arerepresented by an array of pixels. Each pixel of a digitalmulti-spectral image is defined by numerical components that representthe color of the pixel. For example, if a digital multi-spectral imageis described in RGB spectral space, each pixel of the image is definedby three numerical values representing the colors of red, green, andblue.

One of the most common ways of generating a digital multi-spectral imagein RGB spectral space is via a color scanner that is in communicationwith a computer system. The color scanner typically acquires the digitalimage by scanning a source (e.g., a photograph) with sensors sensitiveto three color wavelengths: red, green, and blue. Upon completion of thescan, a digital multi-spectral image of the source is generated in RGBspectral space from the acquired data and can be displayed on a computermonitor or other display device.

Because digital images, and multi-spectral digital images in particular,are described by large amounts of data (e.g., in RGB spectral space,each pixel is described by three numerical values) various compressiontechniques have been developed to compress the image data to provide forthe efficient storage, access and transmission of the digital image.

On a general level, data compression entails the coding of original datainto secondary data, from which, the original data can again be derived.Generally, the secondary or coded data, will be quantitatively less thanthe original data. Data compression falls into two general categories:lossy and lossless. In a lossy system, data is compressed with theknowledge and foresight that the reconstructed data will not be anidentical replica of the original data, but only a close approximation.Conversely, a lossless system is one that produces an exact replica ofthe original data from the compressed data. One well-known compressiontechnique is defined by the JPEG (Joint Photographic Experts Group)standard. A more recent JPEG standard for the lossless and near-losslesscompression of images called JPEG-LS. For more information on JPEG-LS,see Information Technology—Lossless and Near-Lossless Compression ofContinuous-Tone Still Images, ISO/IEC CD 14495:1997(E) March 1997. Otherwell-known compression standards also exist, such as JBIG and GIF.

Lossy compression of image data is generally acceptable because it isknown that the human eye perceives small changes in color lessaccurately than small changes in brightness. Accordingly, small lossesin digital image information that impacts color data caused by lossycompression and decompression are acceptable. Furthermore, displaydevices such as computer monitors and televisions are inherently lossyin that they cannot display all of the information contained in an imageand therefore small losses of image information are difficult toperceive on such lossy devices. Moreover, lossy compression anddecompression techniques offer superior compression ratios compared tolossless compression and decompression techniques.

However, other devices such as digital printers, and digital colorprinters in particular, are far less tolerant of lossy imageinformation. To compound this problem, most digital color printersrequire image information in the CMYK spectral space. Thus, an imagethat is described in RGB spectral space must be transformed into CMYKspectral space before the digital color printer can print it.

In addition to scanning in the RGB color space and printing in the CMYKcolor space, image compression is often done in a third color space.Most JPEG images are compressed in the YCbCr color space. For MPEG-1this is the only allowed color space. The luminance component (Y) iscompressed with better quality than the color components Cb and Cr sincethe human visual system is more forgiving of loss in these colorcomponents. However, the transformation from RGB to YCbCr and back toRGB (or on to CMY) is inherently lossy.

Accordingly, given a lossy image compression and decompression techniqueand a lossy spectral space transformation(s), digital multi-spectralimage information may be significantly lost from the time the image isfirst acquired to the time it is, for example, printed on a digitalcolor printer. The result is often a color image which is lacking inquality as compared to the original image. Hence, a method forcompressing multi-spectral digital images which does not suffer fromthis and other disadvantages is desired.

SUMMARY OF THE INVENTION

According to the present invention, a computer-implemented method forcompressing a digital multi-spectral image represented by a first colorspace is provided. The method includes the steps of: transforming afirst set of first color space components to a plurality of second colorspace components; compressing the second color space components; andreconstructing and transforming the compressed second color spacecomponents into a second set of first color space components such thatthe difference between each of the first set of first color spacecomponents and the second set of first color space components is boundby a predetermined range of values.

It is therefore an advantage of this invention to provide a method forcompressing and reconstructing multi-spectral images with boundedlosses.

BRIEF DESCRIPTION OF THE DRAWINGS

In the accompanying drawings which are incorporated in and constitute apart of the specification, embodiments of the invention are illustrated,which, together with a general description of the invention given above,and the detailed description given below, serve to example theprinciples of this invention.

FIG. 1 is a data flow block diagram of a first embodiment of the presentinvention having compression in two color spaces with one color spacetransformation;

FIG. 2 is a data flow block diagram of a second embodiment of thepresent invention having compression in three color spaces with twocolor space transformations;

FIG. 3 is a data flow block diagram of a third embodiment of the presentinvention having compression in two color spaces with one color spacetransformation and its inverse transformation;

FIG. 4 is a block diagram of an RGB color space to Y′Cb′Cr′ color spacebounded-loss transformer;

FIG. 5 is a block diagram of a Y′Cb′Cr′ color space to RGB color spacebounded-loss transformer;

FIG. 6 is a block diagram of an RGB color space to C′M′Y′ color spacebounded-loss transformer;

FIG. 7 is a block diagram of a C′M′Y′ color space to RGB color spacebounded-loss transformer;

FIG. 8 is a block diagram of a C′M′Y′ color space to traditional CMYcolor space transformer; and

FIG. 9 is block diagram of a system incorporating bounded-loss colorspace transformation.

DETAILED DESCRIPTION OF ILLUSTRATED EMBODIMENTS

Referring now to the drawings, three embodiments of the presentinvention will now be described. The first and third illustratedembodiments employ the compression of a digital multi-spectral image intwo spectral or color spaces. The second illustrated embodiment employsthe compression of a digital multi-spectral image in two spectral orcolor spaces with three spectral or color transformations. In eachembodiment, compressed image data in at least two different spectral orcolor spaces is produced. Additionally, each of the embodiments isillustrated with two components of the spectral space (i.e., Z, Y and A,B) for simplicity. Though the illustrated embodiments are described withregard to only two spectral space components, it should be understoodthat the discussions are equally applicable to all of the components ofa spectral or color space (e.g., third, fourth, fifth, etc.) Moreover,hereinafter the reference to a spectral or color space transformationrefers to the process of transforming multi-spectral image data from afirst color space to a second color space, which may in itself beperformed more than once on different data. Hence an embodiment mayemploy a single color space transformation more than once, as is thecase when two or more sets of data are subject to the same colortransformation, but will still be referred to as employing a singlecolor-space transformation. Accordingly, an embodiment may also employtwo or more color space transformations (i.e., color space 1 to colorspace 2; color space 2 to color space 3; etc.) as part of itscompression/decompression technique. The present discussion will nowfocus on each of the embodiments.

First Embodiment

Two Color Spaces; One Color Space Transformation.

Illustrated in FIG. 1 is a data flow block diagram illustrating thefunctions necessary to compress a multi-spectral image defined by asingle color space into compressed image data representing two colorspaces and accomplished with one color space transformation.Specifically, color space 1 components (Z and Y) of a multi-spectralimage are input into an encoder 102 for data compression. Color space 1may be any color space, such as RGB color space, for example. Datacompression and decompression may be by any known method such as JPEG,GIF, etc., however JPEG baseline compression/decompression is preferred.JPEG baseline compression/decompression provides for the encoding of animage via a top-to-bottom scan. For more information on JPEG, seeInformation Technology—Digital Compression and Coding of Continuous-ToneStill Images—Part 1: Requirements and Guidelines, ITU-T RecommendationT.81 (1992)|ISO/IEC 10918-1:1993. Alternatively, proprietarycompression/decompression techniques may also be employed.

The encoder 102 outputs compressed color space 1 data to two paths. Onepath of compressed color space 1 data is input into output function 116for incorporation into a final color space 1 and 2 compressed datastream. A second path of compressed color space 1 data is input into adecoder 104 to generate reconstructed color space 1 components (Z′ andY′). Since the compression/reconstruction performed by encoder 102 anddecoder 104 is lossy, in the case of a lossy compression/decompressiontechnique, reconstructed color space 1 components (Z′ and Y′) are notidentical to the originally compressed color space 1 components (Z andY). The encoder 102 and decoder 104 are preferably included within aprimary encoder 105 since the process of encoding can include thesub-process of reconstructing the encoded data. This reconstruction isperformed because the encoder must be privy to the decoder'sdecompression behavior.

The reconstructed color space 1 components (Z′ and Y′) are output fromdecoder 104 and input into color space 1 to color space 2 transformer106B to generate a first set of color space 2 components. Color space 1to color space 2 transformer 106B, as well as all other color spacetransformers hereinafter described, is preferably software logic whichincorporates conventional color space transformation algorithms.Alternatively, hardware logic incorporating the color spacetransformation algorithms may also be employed. Conventional color spacetransformation algorithms are well-known. In the present embodiment,color space 1 may be RGB color space and color space 2 may be YCbCrcolor space. Other color space combinations are also possible.

The output of color space 1 to color space 2 transformer 106B includesthe output of each component of color space 2 that is generated fromreconstructed color space 1 components (Z′ and Y′). The illustratedembodiment shows only two outputs for clarity. Just as the reconstructedcolor space 1 components (Z′ and Y′) were transformed to color space 2components, the uncompressed color space 1 components (Z and Y) areinput into color space 1 to color space 2 transformer 106A from which asecond set of color space 2 components are generated. The correspondingcolor space 2 components generated from transformers 106A and 106B areinput into difference functions 110 and 112. The output of differencefunctions 110 and 112 are input into encoder 114 for data compression.As described above, the data compression may be via any known technique,however, differential JPEG compression is preferred. In differentialJPEG compression, difference data between the original component dataand encoder reconstructed inverse transformed component data is coded asa differential JPEG frame. For more information on JPEG, see InformationTechnology—Digital Compression and Coding of Continuous-Tone StillImages—Part 1: Requirements and Guidelines, ITU-T Recommendation T.81(1992)|ISO/IEC 10918-1:1993. Alternatively, anycompression/decompression technique capable of handling differences mayalso be employed, including proprietary techniques. Accordingly, theoutput of encoder 114 is input into output function 116 forincorporation into a final color space 1 and 2 compressed data stream.The output of encoder 114 represents the loss or error introduced by thelossy compression/reconstruction of color space 1 components in terms ofcolor space 2 difference data.

The compressed data, both color space 1 and difference color space 2data, can now be exported to a file or other device, such as a printer,for processing. By knowing the error introduced by the lossy compressionand reconstruction of the color space 1 multi-spectral image, acorrection based on the color space 2 difference data can be used duringthe subsequent decompression and transformation of compressed colorspace 2 data to provide a less-lossy or lossless compression anddecompression process. This is especially desirable when thedecompressed multi-spectral image is desired to be defined by colorspace 2 components.

Second Illustrated Embodiment

Three Color Spaces; Two Color Space Transformations.

Referring now to FIG. 2, a data flow block diagram illustrating thefunctions necessary to compress a multi-spectral image defined by asingle color space into compressed image data representing two colorspaces and accomplished with two color space transformations. Moreparticularly, color space 1 components (A and B) are input into a colorspace 1 to color space 2 transformer 202B. For example, color space 1 tocolor space 2 transformer 202B may transform RGB color space componentsinto CIE L* a* b* color space components. The color space 2 componentsare input into encoder 102 for compression. The encoder 102 outputscompressed color space 2 data to two paths. One path of compressed colorspace 2 data is input into output function 116 for incorporation intothe final color space 2 and 3 compressed data stream. A second path ofcompressed color space 2 data is input into a decoder 104 to generatereconstructed color space 2 components (Z′ and Y′). Since thecompression/reconstruction performed by encoder 102 and decoder 104 islossy, the reconstructed color space 2 components (Z′ and Y′) are notidentical to the originally compressed color space 2 components (Z andY).

The reconstructed color space 2 components (Z′ and Y′) are output fromdecoder 104 and input into color space 2 to color space 3 transformer204B to generate a first set of color space 3 components. Color space 3may be CMYK color space, or any other color space. The output of colorspace 2 to color space 3 transformer 204B includes the output of eachcomponent of color space 3 that is generated from the reconstructedcolor space 2 components (Z′ and Y′). As described above, only twocomponents are shown for clarity.

The color space 1 components (A and B) are further input into colorspace 1 to color space 3 transformer 206. The output transformer 202Aincludes all of the components of color space 2 and are input into colorspace 2 to color space 3 transformer 204A. The corresponding color space3 components generated from transformers 204A and 204B are input intodifference functions 110 and 112. The output of difference functions 110and 112 are input into encoder 114 for data compression. The output ofencoder 114 is input into output function 116 for incorporation into afinal color space 2 and 3 compressed data stream. The output of encoder114 represents the loss or error introduced by the lossycompression/reconstruction of color space 2 components in terms of colorspace 3 difference data.

Similar to the embodiment of FIG. 1, the errors due to the lossycompression and reconstruction of the color space 2 multi-spectralimage, and possibly also the color space transformation from color space2 to color space 3, can be corrected by the differential color space 3data upon the subsequent decompression and transformation of thecompressed color space 2 data to produce a less-lossy or losslesscompression and decompression process. This is especially desirable whenthe decompressed, multi-spectral image is desired to be ultimatelydefined by color space 3 components.

Third Illustrated Embodiment

Two Color Spaces; One Color Space Transformation.

Illustrated in FIG. 3 is a data flow block diagram illustrating thefunctions necessary to compress a multi-spectral image defined by asingle color space into compressed image data representing two colorspaces and accomplished with one color space transformation and itsinverse. Specifically, color space 1 components (A and B) are input intocolor space 1 to color space 2 transformer 302 to generate color space 2components (Z and Y). The color space 2 components are input intoencoder 102 for compression. The encoder 102 outputs compressed colorspace 2 data to two paths. One path of compressed color space 2 data isinput into output function 116 for incorporation into the final colorspace 2 and 1 compressed data stream. A second path of compressed colorspace 2 data is input into a decoder 104 to generate reconstructed colorspace 2 component data. In as much as the compression/reconstructionperformed by encoder 102 and decoder 104 is lossy, reconstructed colorspace 2 components (Z′ and Y′) are not identical to the originallycompressed color space 2 components (Z and Y).

The reconstructed color space 2 components are output from decoder 104and input into color space 2 to color space 1 transformer 304 togenerate a new set of color space 1 components. The new color space 1components are input into difference functions 110 and 112.Additionally, the original color space 1 components are correspondinglyalso input into difference functions 110 and 112 to generate color space1 difference data. The output of difference functions 110 and 112 areinput into encoder 114 for data compression. As described above, thedata compression may be via any known technique, however, differentialJPEG compression is preferred. Accordingly, the output of encoder 114 isinput into output function 116 for incorporation into a final colorspace 2 and 1 compressed data stream. The output of encoder 114represents the loss or error introduced by the lossy transformation ofcolor space 1 into color space 2 and back to color space 1 and the lossycompression/decompression of color space 2 components.

In all of the embodiments, decompression of the compressed image data isaccomplished by first separating each color space from the mixed colorspace compressed data stream output by output function 116. Eachcompressed color space is then reconstructed via the technique employedduring compression (e.g., JPEG baseline and differential JPEG).Thereafter, each component of the reconstructed color spaces will beavailable for further processing.

It should be further noted that the present invention is applicable tothe acquisition, compression, and decompression of satellite imagery.More particularly, satellites acquire images with a plurality ofscanners having a plurality of scan frequencies. These frequenciesinclude the visible spectrum and the non-visible spectrum, such as theultraviolet, infrared, and radio frequency spectrums. Consequently,satellite images are typically comprised of spectral spaces having morethan two components. Each spectral space component of a satellite imageis traditionally referred to as a “band.” Landsat 5 images, for example,are defined by a spectral space having seven bands. According, thepresent invention may be employed to generate a custom color space whichmixes, or de-correlates, the original color space bands and which ismore compressible than the original color space. The custom color spacedoes not have to have the same number of bands as the original colorspace. Reconstruction and transformation back to the original colorspace provides for the determination of difference data between theoriginal color space and the reconstructed and transformed originalcolor space. The compressed custom color space components and thecompressed difference data, defined in terms of the original color spacecomponents, can then be assembled into a compressed data stream.Alternatively, bounded-loss transformations to a custom color space thatis more compressible than the original color space may be employed.

Compression of Multi-Spectral Images with Bounded Loss.

It has been determined by the present inventors that the compression anddecompression of multi-spectral images can be performed with boundedlosses. To recall, color space transformation from one color space toanother color space can be lossy. The present invention provides atechnique for performing bounded-loss transformations by designingtransformations such that the loss incurred is within a known range. Animportant characteristic in the design of the bounded-losstransformations of the present invention is noise-level present in theimage. Noise is introduced into the image through the process ofdigitizing an analog image. Specifically, noise is introduced during thedigitizing round-off process which results in the necessary integerprecision color space components. Accordingly, a transformation designedwith a bounded loss equal to or less than the image noise-level will notsignificantly degrade the image quality. Additionally, because the lossor error is bound, it may be corrected for upon reconstruction of thecompressed color space data (e.g., color space 1) and transformation ofthe reconstructed color space data (e.g., color space 1) to the colorspace of the reconstructed difference data (e.g., color space 2). Thepresent discussion will now discuss the bounded-loss transformations ofthe present invention for the three most widely employed color spaces(i.e., RGB, CMY, and YCbCr) and the transformations: (1) RGB color spaceto Y′Cb′Cr′ color space and Y′Cb′Cr′ color space back to RGB color spaceand (2) RGB color space to C′M′Y′ color space and C′M′Y′ color spaceback to RGB color space.

Referring now to FIGS. 4 and 5, bounded-loss color transformers for twocolor spaces are shown which bound the loss to ±1 (where 1 is, forexample, the noise-level of an image). More particularly, an RGB colorspace to Y′Cb′Cr′ color space bounded-loss transformer 402 isillustrated in FIG. 4 and a Y′Cb′Cr′ color space to RGB color spacebounded-loss transformer 502 is shown in FIG. 5. In the embodiments, R,G, B, and Y′, Cb′, Cr′ are integer values from 0 to at most 255 for8-bit images. The notation Y′ Cb′ Cr′ denotes a non-conventionaltransform.

Referring now to FIG. 4 specifically, the inputs to transformer 402 arethe RGB color space components R, G, and B. The transformer 402 appliesthe following formulas (1)-(4) to arrive at the Y′Cb′Cr′ color spacecomponents Y′, Cb′, and Cr′:

Y′=Round (a R+b G+c B)  (1)

Cb′=Round ((B−Y′)/3+128)  (2)

Cr′=Round ((R−Y′)/3+128)  (3)

where a+b+c=1  (4)

Referring now to FIG. 5 specifically, the inputs to transformer 502 arethe Y′Cb′Cr′ color space components Y′, Cb′, and Cr′. The transformer502 applies the following formulas to arrive at the RGB color spacecomponents R1, G1, and B1:

R1=(Cr*+Y′)  (5)

G1=Round (Y′−((a/b) Cr*)−((c/b) Cb*))  (6)

B1=(Cb* +Y)  (7)

 where:Cb*=3(Cb′−128)  (8)

Cr*=3(Cr′−128)  (9)

The term “Round” indicates a rounding operation to arrive at an integervalue.

It can be further determined that the application of transformationformulas (1)-(9) for certain a, b, and c values provides fortransformations that yield losses which are less than or equal to 1(i.e., the designed acceptable loss). The values of a, b, and c arechosen such that the loss introduced in the RGB components R and B mustnot yield a larger loss than desired in G. Though an exhaustive searchfor these values has not been performed, illustrated in Table 1 arevalues which have been exhaustively tested and which satisfy the abovecriteria. Accordingly, the values listed in Table 1 provide that|R−R1|≦1, |G−G1|≦1, and |B−B1|≦1.

TABLE 1 (a, b, c) Range Y′ Range Cb′ Range Cr′ (0.2, 0.7, 0.1) 0, 25552, 204 60, 196 (0.25, 0.65, 0.1) 0, 255 52, 204 64, 192 (0.28, 0.62,0.1) 0, 255 51, 204 66, 190 (0.29, 0.61, 0.1) 0, 255 51, 204 68, 188

Referring now to FIGS. 6 and 7, bounded-loss color transformers for twocolor spaces (RGB to C′M′Y′ and C′M′Y′ to RGB) are shown which bound theloss to +1. More particularly, an RGB color space to C′M′Y′ color spacebounded-loss transformer 602 is illustrated in FIG. 6 and a C′M′Y′ colorspace to RGB color space bounded-loss transformer 702 is shown in FIG.7. In the embodiments, R. G, B. and C′, M′, Y′ are integer values from 0to at most 255 for 8-bit images. The notation C′M′Y′ denotes anon-conventional transform.

Referring now to FIG. 6 specifically, the inputs to transformer 602 arethe RGB color space components R, G, and B. The transformer 602 appliesthe following formulas (10)-(12) to arrive at the C′M′Y′ color spacecomponents C′, M′, and Y′:

C′=Round ((255−R )/3 )  (10)

M′=Round ((255−G )/3 )  (11)

Y′=Round ((255−B )/3 )  (12)

Referring now to FIG. 7 specifically, the inputs to transformer 702 arethe C′M′Y′ color space components C′, M′and Y′. The transformer 702applies the following formulas to arrive at the RGB color spacecomponents R1, G1, and B1:

R1=(255−3 (C′))  (13)

G1=(255−3 (M′))  (14)

B1=(255−3 (Y′))  (15)

Just as in the case of the transformers of FIGS. 4 and 5, it has beendetermined that the application of transformation formulas (10)-(15)provide transformation losses which are less than or equal to 1 (i.e.,the noise-level). Specifically, the differences between the original RGBcolor space components and the reconstructed RGB color space componentsare such that |R−R1|≦1, |G−G1|≦1, and |B−B1|≦1.

A general approach to bounding the loss in an RGB to C′M′Y′ color spacetransform and a C′M′Y′ to RGB color space transform will now bepresented. Specifically, the bounded-loss RGB color space to C′M′Y′color space transformation is governed by the following equations(16)-(18):

 C′=Round ((255−R )/D)  (16)

M′=Round ((255−G )/D)  (17)

Y′=Round ((255−B )/D)  (18)

The bounded-loss C′M′Y′ color space to RGB color space transformation isgoverned by the following equations (19)-(21):

R1=(255−D (C′))  (19)

G1=(255−D (M′))  (20)

B1=(255−D (Y′))  (21)

In equations (16-21), the variable D is represented by equation (22):

D=2(E )+1  (22)

where the variable E is the loss or error by which the transformationshall be bound. The application of equations (16)-(22) results in thedifferences between the original RGB color space components and thereconstructed RGB color space components being |R−R1|≦E, |G−G1|≦E, and|B−B1|≦E.

Illustrated in FIG. 8 is C′M′Y′ color space to traditional CMY colorspace transformer 802. The transformer 802 is governed by the followingequations:

C=3(C′)  (23)

M=3(M′)  (24)

Y=3(Y′)  (25)

A lossless compression of the C′M′Y′ components results in at most a ±1error after the transformation to CMY color space. If the C′M′Y′components are compressed with bounded error, the error bounds areadded.

Referring now to FIG. 9, a block diagram of a compression/decompressionsystem incorporating color space transformations with bounded losses isshown. Specifically, the system includes color space 1 to color space 2transformer 902, encoder 904, decoder 906, and color space 2 to colorspace 1 transformer 908. Bounded-loss color space transformers 902 and908 may be any bounded-loss color space transformer created according tothe present invention, such as the transformers of FIGS. 4 and 5 orFIGS. 6 and 7. The encoder 904 and decoder 906 are preferably JPEGbaseline encoders and decoders. Alternatively, they may be createdaccording to any known compression/decompression technique which iscapable of handling color space component data.

The system of FIG. 9 operates by inputting color space 1 components(e.g., A and B) into color space 1 to color space 2 transformer 902.Color space 1 to color space 2 transformer 902 generates color space 2components (e.g., Z and Y) which are input into encoder 904. Encoder 904generates a compressed data stream which is ultimately input intodecoder 906 to generate reconstructed color space 2 components (e.g., Z′and Y′). The reconstructed color space 2 components (e.g., Z′ and Y′)are input into and color space 2 to color space 1 transformer 908 togenerate color space 1 components (e.g., A′ and B′). The differencebetween the reverse transformed color space 1 components (e.g., A′ andB′) and the original color space 1 components (e.g., A and B) isaccordingly bounded by the loss due to the color space transformers 902and 906 and any loss from the compression/decompression by encoder 904and decoder 906.

Referring now back to FIGS. 1, 2, and 3, the present invention furtherprovides that transformers 106A,B and 202A,B and 204A,B and 206 and 302and 304 may be bounded-loss transformers and that encoders 102 and 114and decoder 104 may be employing a lossless compression technique. Inthis configuration, difference values are determined from thebounded-loss transformations, while a lossless compression technique isemployed. Other combinations are also possible.

The embodiments of the present invention may be implemented as softwareor hardware logic in a general personal or specific purpose computersystem. The computer system may be a desktop, floor standing, orportable microcomputer that is comprised of a system unit having acentral processing unit (CPU) and associated volatile and non-volatilememory, including all RAM and BIOS ROM, a system monitor, a keyboard,one or more flexible diskette drives, a fixed disk storage drive (alsoknown as a “hard drive”), CD-ROM drive, a “mouse” pointing device, andan optional printer. The computer system may further include modem ornetwork connection devices which allow it to communicate with hostcomputer systems. Examples of such personal computer systems includeIBM's APTIVA® and THINKPAD® computer systems.

As mentioned, these computer systems may employ the present invention assoftware or hardware logic. The software logic includes code written tobe executed by the computer system's central processor or otherprocessor, such as an on-board or board added DSP (Digital SignalProcessor) and/or micro-controller. The code may be stored on acomputer-readable medium such as a floppy disk or CD-ROM. Hardware logicembodiments include the present invention embodied in the form of anASIC (Application Specific Integrated Circuit), combination of ASIC's,and/or a programmed micro-controller. The code may further be embodiedin a PROM or EEPROM which is readable by a computer system, processor,or controller.

While the present invention has been illustrated by the description ofembodiments thereof, and while the embodiments have been described inconsiderable detail, it is not the intent of the applicants to restrictor in any way limit the scope of the appended claims to such detail.Additional advantages and modifications will readily appear to thoseskilled in the art. For example, other bounded-loss colortransformations may be derived via the teachings of the presentinvention. Therefore, the invention, in its broader aspects, is notlimited to the specific details, the representative apparatus, andillustrative examples shown and described. Accordingly, departures maybe made from such details without departing from the spirit or scope ofthe applicant's general inventive concept.

We claim:
 1. A computer-implemented method for compressing a digitalmulti-spectral image represented by a first color space, the methodcomprising the steps of: (a) transforming a first set of first colorspace components to a plurality of second color space components,wherein this step of transforming comprises the step of inputting thefirst set of first color space components into a first transformerhaving the following transformation equations embedded therein: C′=Round((255−R)/D) M′=Round ((255−G)/D) Y′=Round ((255−B)/D) and D=2 (E )+1,wherein E is the desired loss and equal to one, and wherein the firstcolor space comprises an RGB color space and the second color spacecomprises a C′M′Y′ color space; (b) compressing the second color spacecomponents; and (c) reconstructing and transforming the compressedsecond color space components into a second set of first color spacecomponents such that the difference between each of the first set offirst color space components and the second set of first color spacecomponents is bound by a predetermined range of values.
 2. Acomputer-implemented method for compressing a digital multi-spectralimage represented by a first color space, the method comprising thesteps of: (a) transforming a first set of first color space componentsto a plurality of second color space components, wherein this step oftransforming comprises the step of inputting the first set of firstcolor space components into a first transformer having the followingtransformation equations embedded therein: C′=Round ((255−R)/D) M′=Round((255−G)/D) Y′=Round ((255−B)/D) and D=2 (E )+1, wherein E is thedesired loss and equal to one, and wherein the first color spacecomprises an RGB color space and the second color space comprises aC′M′Y′ color space; (b) compressing the second color space components;and (c) reconstructing and transforming the compressed second colorspace components into a second set of first color space components suchthat the difference between each of the first set of first color spacecomponents and the second set of first color space components is boundby a predetermined range of values wherein this step of reconstructingand reconfiguring comprises the step of inputting the second color spacecomponents into a second transformer having the following transformationequations embedded therein: R1=(225−D(C′)) G1=(255−D(M′))B1=(255−D(Y′)).
 3. A computer-implemented method for compressing adigital multi-spectral image represented by a first color space, themethod comprising the steps of: (a) transforming a first set of firstcolor space components to a plurality of second color space componentsvia a first bounded-loss color space transformer by inputting said firstset of first color space components into said first bounded-loss colorspace transformer having the following bounded-loss transformationequations embedded therein: Y′=Round (aR+bG+cB) Cb′=Round ((B−Y′)/3+128)Cr′=Round ((R−Y′)/3+128) where a+b+c=1 and wherein the first color spacecomprises an RGB color space and the second color space comprises anY′Cb′Cr′ color space; (b) compressing the second color space components;and (c) reconstructing and transforming the compressed second colorspace components into a second set of first color space components via asecond bounded-loss color space transformer such that the differencebetween each of the first set of first color space components and thesecond set of first color space components is bound by a predeterminedrange of values as determined by the first and second bounded-loss colorspace transformers.
 4. The method of claim 3 wherein the step ofreconstructing and transforming the compressed second color spacecomponents into a second set of first color space components such thatthe difference between each of the first set of first color spacecomponents and the second set of first color space components is boundby a predetermined range of values comprises the step of inputting thesecond color space components into a second transformer having thefollowing transformation equations embedded therein: R1=(Cr*+Y′)G1=Round (Y′−((a/b) Cr*)−((c/b) Cb*)) B1=(Cb*+Y′) where: Cb*=3(Cb′−128)Cr*=3(Cr′−128) and wherein the first color space comprises an RGB colorspace and the second color space comprises an Y′Cb′Cr′ color space.
 5. Acomputer-implemented method for compressing a digital multi-spectralimage represented by a first color space, the method comprising thesteps of: (a) transforming a first set of first color space componentsto a plurality of second color space components via a first bounded-losscolors space transformer comprises the step of inputting the first setof first color space components into the first bounded-loss color spacetransformer having the following transformation equations embeddedtherein: C′=Round ((255−R)/D) M′=Round ((255−G)/D) Y′=Round ((255−B)/D)and D=2(E)+1, wherein E is the desired loss greater than zero andwherein the first color space comprises an RGB color space and thesecond color space comprises a C′M′Y′ color space; (b) compressing thesecond color space components; and (c) reconstructing and transformingthe compressed second color space components into a second set of firstcolor space components via a second bounded-loss color space transformersuch that the difference between each of the first set of first colorspace components and the second set of first color space components isbound by a predetermined range of values as determined by the first andsecond bounded-loss color space transformers.
 6. The method of claim 5wherein the step of reconstructing and transforming the compressedsecond color space components into a second set of first color spacecomponents such that the difference between each of the first set offirst color space components and the second set of first color spacecomponents is bound by a predetermined range of values comprises thestep of inputting the second color space components into a secondtransformer having the following transformation equations embeddedtherein: R1=(255−D(C′)) G1=(255−D(M′)) B1=(255−D(Y′)) and D=2 (E)+1,wherein E is the desired loss greater than zero and wherein the firstcolor space comprises an RGB color space and the second color spacecomprises an C′M′Y′ color space.
 7. The method of claim 6 wherein E isequal to one.
 8. The method of claim 5 wherein E is equal to one.